Detailed scientific analysis in the form of modeling (e.g. demand modeling), and the discrimination of variables (e.g. optimal pricing variables), has often been used. However, actual customer transactions involve deal negotiations and customer focused pricing steps that can result in significant deviations from an optimal price.
Modeling and planning (e.g. economic and financial modeling) are commonly used to estimate or predict the performance and outcome of real systems, given specific sets of input data of interest. A modeled system (e.g. an economic-based system) may have many variables and influences (e.g. factors or inputs) which determine its behavior. A model is a mathematical expression or representation which predicts the outcome or behavior of the system under a variety of conditions. It is possible to review a historical data set, understand its past performance, and state with relative certainty that the system's past behavior was indeed driven by the historical data set. A more difficult task is to generate a mathematical model of the system which predicts how the system will behave, or would have behaved, with different sets of data and assumptions. While forecasting and backcasting using different sets of input data is inherently imprecise, i.e., no model can achieve 100% certainty, the field of probability and statistics has provided many tools which allow such predictions to be made with reasonable certainty and acceptable levels of confidence. Backcasting may be understood as starting from a desirable future and identifying ways to achieve the desirable future.